1. MATH 2311
Section 3.1

2. Random Variables
A random variable is a variable whose value is a numerical outcome of a random phenomenon. It assigns one and only one numerical value to each point in the sample space for a random experiment. A discrete random variable is one that can assume a countable number of possible values. A continuous random variable can assume any value in an interval on the number line. A probability distribution table of X consists of all possible values of a discrete random variable with their corresponding probabilities.

3. Example:
Suppose a family has 3 children. Show all possible gender combinations:

4. Example:
Suppose a family has 3 children. Show all possible gender combinations: Keep in mind, there will be (2)(2)(2)=8 combinations. BBB BGB GBB GGB BBG BGG GBG GGG

5. Example:
Suppose a family has 3 children. Now suppose we want the probability distribution for the number of girls in the family. Draw a probability distribution table for this example.

6. Example:
Suppose a family has 3 children. Now suppose we want the probability distribution for the number of girls in the family. Draw a probability distribution table for this example. We need to know the total possible outcomes. We need to categorize them by number of girls. We need a probability of each outcome.

7. Example:
Suppose a family has 3 children. Now suppose we want the probability distribution for the number of girls in the family. Draw a probability distribution table for this example. 0 girls: 1 girl: 2 girls: 3 girls:

8. Example:
Find the following probabilities: 0 girls: 1 girl: 2 girls: 3 girls:

9. Example: Popper # 4 1. 2. 3. 4.
0.10
0.15
0.30
0.05
0.10
0.15
0.20
0.05
0.50
0.15
0.20
0.25
0.50
0.75
0.80
0.70

10. Expected Value:
The mean, or expected value, of a random variable X is found with the following formula:

11. What is the expected number of girls in the family above?

12. What is the expected number of girls in the family above. In R Studio:
What is the expected number of girls in the family above? In R Studio: assign(“x”,c(values)) assign(“p”,c(probabilities)) sum(x*p) TI: x  L1, p  L2, 2nd List (STAT), MATH (right arrow), sum (option 5) sum(L1*L2)

13. Variance and Standard Deviation
Or (the alternate formula)
Repeat the Expectancy Formula using x2 instead of x.

14. Variance and Standard Deviation
In R Studio: sum((x-mean)^2*p) or
sum(x^2*p)-sum(x*p)^2
In TI: sum(L1^2*L2)-sum(L1*L2)^2

15. Find the standard deviation for the number of girls in the example above

16. Popper 4 continued: 5.
What is the expected value? The variance (6) and standard deviation (7)?
3.50
4.20
0.35
5.00
6, 7.
3.66
13.39
1.91
1.55

17. Rules for means and variances:
Suppose X is a random variable and we define W as a new random variable such that W = aX + b, where a and b are real numbers. We can find the mean and variance of W with the following formula:

18. Rules for means and variances:
Likewise, we have a formula for random variables that are combinations of two or more other independent random variables. Let X and Y be independent random variables,

19. Suppose you have a distribution, X, with mean = 22 and standard deviation = 3. Define a new random variable Y = 3X + 1.